- Basics
- Scalable Complexity via Scrolling
- Images and Graphics
- References
November 21, 2020
Rscript -e "rmarkdown::render('ioslide_template.Rmd'); knitr::knit('ioslide_template.Rmd', tangle=TRUE)"
\[f(k) = {n \choose k} p^{k} (1-p)^{n-k} \tag{1}\] \[\sigma_{M} = \frac{\sigma}{\sqrt(N)} \tag{2}\]
bibtex.bib and then citing the them in the text with this syntax [@refid1; @refid2]z <- "dajfdfkfffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff" z z z z z z z z z
data.frames is autmatically paged when including df_print: paged in preamble. In addition, one can set how many rows are shown on each page by assigning the desired number to the rows.print argument in the header of the corresponding code chunk (e.g. below it is set to 75 rows).x <- cbind(iris, iris[,5:1]) x
sbatchPrint information about queues/partitions available on a cluster.
sinfo
Compute jobs are submitted with sbatch via a submission script (here script_name.sh).
sbatch script_name.sh
Sample submission script
#!/bin/bash -l #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=1 #SBATCH --mem-per-cpu=1G #SBATCH --time=1-00:15:00 # 1 day and 15 minutes #SBATCH --mail-user=useremail@address.com #SBATCH --mail-type=ALL #SBATCH --job-name="some_test" #SBATCH -p batch # Choose queue/parition from: intel, batch, highmem, gpu, short myscript.sh
Interactive session with specific resource requests
srun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 1:00:00 --pty bash -l
library(DT) datatable(iris)
'smaller: false' is used. You usually have it set to 'smaller: true''{.smaller}' at the end of a slide title{.flexbox .vcenter} option after the title of a slideThis can be useful to have a figure on the right and bullets describing it on the left.
library(dplyr); library(ggplot2); library(reshape2)
iris %>%
group_by(Species) %>%
summarize_all(mean) %>%
reshape2::melt(id.vars=c("Species"), variable.name = "Samples", value.name="Values") %>%
ggplot(aes(Samples, Values, fill = Species)) +
geom_bar(position="dodge", stat="identity")
Howard, Brian E, Qiwen Hu, Ahmet Can Babaoglu, Manan Chandra, Monica Borghi, Xiaoping Tan, Luyan He, et al. 2013. “High-Throughput RNA Sequencing of Pseudomonas-Infected Arabidopsis Reveals Hidden Transcriptome Complexity and Novel Splice Variants.” PLoS One 8 (10): e74183. https://doi.org/10.1371/journal.pone.0074183.
Huber, Wolfgang, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nat. Methods 12 (2): 115–21. https://doi.org/10.1038/nmeth.3252.
Kim, Daehwan, Geo Pertea, Cole Trapnell, Harold Pimentel, Ryan Kelley, and Steven L Salzberg. 2013. “TopHat2: Accurate Alignment of Transcriptomes in the Presence of Insertions, Deletions and Gene Fusions.” Genome Biol. 14 (4): R36. https://doi.org/10.1186/gb-2013-14-4-r36.
Langmead, Ben, and Steven L Salzberg. 2012. “Fast Gapped-Read Alignment with Bowtie 2.” Nat. Methods 9 (4). Nature Publishing Group: 357–59. https://doi.org/10.1038/nmeth.1923.
Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T Morgan, and Vincent J Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Comput. Biol. 9 (8): e1003118. https://doi.org/10.1371/journal.pcbi.1003118.
Li, H, and R Durbin. 2009. “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform.” Bioinformatics 25 (14): 1754–60. https://doi.org/10.1093/bioinformatics/btp324.
Li, Heng. 2013. “Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM.” arXiv [Q-bio.GN]. http://arxiv.org/abs/1303.3997.
Liao, Yang, Gordon K Smyth, and Wei Shi. 2013. “The Subread Aligner: Fast, Accurate and Scalable Read Mapping by Seed-and-Vote.” Nucleic Acids Res. 41 (10): e108. https://doi.org/10.1093/nar/gkt214.